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Magneto-optical (MO) imaging using MO imaging plates is a magnetic imaging technique that enables real-time measurements and is expected to be used for non-destructive testing and for observing the magnetic domains of magnetic materials. In this study, we propose a quantitative measurement method for three dimensional (3D) magnetic field vector measurements. The x-and y-components of the magnetic fields within the measured plane are calculated from the measured z-component by using a signal transformation method based on magnetic field transfer functions. Furthermore, the magnetic field distributions at different heights are also obtained from a one-shot image. In this paper, 3D magnetic field vector measurements are demonstrated for ferrite magnets and an electrical steel sheet.

The optically induced magnetization and ultrafast spin relaxation in an antiferromagnet MnO were observed by polarization spectroscopy with the pump-probe technique. The spin relaxation time in the picosecond region was measured at temperatures from 6 up to 800 K. The observed spin relaxation is the sum of the spin-spin relaxation and the spin-lattice relaxation. At lower temperatures below room temperature, the temperatureindependent spin-spin relaxation is dominant. A stepped decrease in the spin relaxation rate was observed near the Néel temperature T N = 118 K, where the long-range order is lost. At higher temperatures above room temperature, the temperature-dependent spin-lattice relaxation is dominant. The observed spin-lattice relaxation rate has a T 2 dependence instead of the T 9 dependence well known in magnetic-resonance measurements for the Raman process of phonons. The observed temperature dependence can be explained by the conventional theory of spin-lattice relaxation for the Raman process by taking account of the effect of the Debye temperature of the crystal.

We propose a magneto-optical diffractive deep neural network (MO-D2NN). We simulated several MO-D2NNs, each of which consists of five hidden layers made of a magnetic material that contains 100 × 100 magnetic domains with a domain width of 1 µm and an interlayer distance of 0.7 mm. The networks demonstrate a classification accuracy of > 90% for the MNIST dataset when light intensity is used as the classification measure. Moreover, an accuracy of > 80% is obtained even for a small Faraday rotation angle of π/100 rad when the angle of polarization is used as the classification measure. The MO-D2NN allows the hidden layers to be rewritten, which is not possible with previous implementations of D2NNs.

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